Risk assessment in COVID‐19: Prognostic importance of cardiovascular parameters

Abstract Background Cardiovascular risk factors and comorbidities are highly prevalent among COVID‐19 patients and are associated with worse outcomes. Hypothesis We therefore investigated if established cardiovascular risk assessment models could efficiently predict adverse outcomes in COVID‐19. Furthermore, we aimed to generate novel risk scores including various cardiovascular parameters for prediction of short‐ and midterm outcomes in COVID‐19. Methods We included 441 consecutive patients diagnosed with SARS‐CoV‐2 infection. Patients were followed‐up for 30 days after the hospital admission for all‐cause mortality (ACM), venous/arterial thromboembolism, and mechanical ventilation. We further followed up the patients for post‐COVID‐19 syndrome for 6 months and occurrence of myocarditis, heart failure, acute coronary syndrome (ACS), and rhythm events in a 12‐month follow‐up. Discrimination performance of DAPT, GRACE 2.0, PARIS‐CTE, PREDICT‐STABLE, CHA2‐DS2‐VASc, HAS‐BLED, PARIS‐MB, PRECISE‐DAPT scores for selected endpoints was evaluated by ROC‐analysis. Results Out of established risk assessment models, GRACE 2.0 score performed best in predicting combined endpoint and ACM. Risk assessment models including age, cardiovascular risk factors, echocardiographic parameters, and biomarkers, were generated and could successfully predict the combined endpoint, ACM, venous/arterial thromboembolism, need for mechanical ventilation, myocarditis, ACS, heart failure, and rhythm events. Prediction of post‐COVID‐19 syndrome was poor. Conclusion Risk assessment models including age, laboratory parameters, cardiovascular risk factors, and echocardiographic parameters showed good discrimination performance for adverse short‐ and midterm outcomes in COVID‐19 and outweighed discrimination performance of established cardiovascular risk assessment models.

outweighed discrimination performance of established cardiovascular risk assessment models. Due to enormous pro-thrombotic and fibrinolytic imbalance in COVID-19, a significantly higher prevalence of thromboembolic complications compared to other critical illnesses is reported. 5 Pulmonary embolism (PE) and deep vein thrombosis (DVT) are the most common COVID-19-associated vascular complications. 6,7 According to previous studies, PE occurs more frequently than DVT. 6,7 Therefore, PE is suggested to develop rather from local immunothrombotic processes in pulmonary vasculature (microangiopathy vs. macroangiopathy) than from embolic complications due to DVT. 8 Arterial thrombotic events seem to be less common than venous thromboembolism (VTE). According to Klok et al., 6 arterial thrombotic events occurred in 3.7% of critically ill COVID-19 patients. Bilaloglu et al. 9 reported an 8.9% prevalence of AMI in 3334 intensive care unit (ICU) and non-ICU patients.
A variety of risk assessment models for prediction of mortality and/or ICU treatment among COVID-19 patients exist, for example, the Quick COVID-19 Severity Index (qCSI), COVID-GRAM, 4C Mortality Score. [10][11][12] A score by Galloway et al. and The Veterans Health Administration COVID-19 (VACO) Index are among the few ones which include cardiovascular risk factors (CVRFs), for example, arterial hypertension and diabetes mellitus. 13,14 Risk assessment models considering cardiac biomarkers or echocardiographic parameters are still rare. We recently showed that a multivariable model including N-terminal prohormone of brain natriuretic peptide (NT pro-BNP), TnI, and D-dimer showed good discrimination performance for mechanical ventilation and ACM in a 30-day follow-up period.
However, echocardiographic parameters failed to discriminate between favorable and adverse outcomes in COVID-19 patients. 15 Previous data strongly suggest an association between cardiovascular burden and increased mortality in COVID-19. Hence, we sought to examine whether existing risk assessment models, originally established to predict the risk of thromboembolic and bleeding complications in CVD patients, may help to identify COVID-19 patients at high risk for an unfavorable course of disease. We evaluated GRACE 2.0 score which was developed on the basis of a global registry including 102.341 acute coronary syndrome (ACS) patients and aims to predict the risk of ACM up to 3 years after an ACS as well as the risk of myocardial infarction (MI) after 1 year. 16,17 We also included the well-known CHA 2 DS 2 -VASc score assessing the risk of stroke and thromboembolism in patients with atrial fibrillation (AF). 18 Several scores dealing specifically with CAD patients were also evaluated, for example, DAPT, PREDICT-STABLE, and PARIS-CTE which help decide on the duration of dual antiplatelet therapy (DAPT) following a percutaneous coronary intervention (PCI) aiming to lower the occurrence of thromboischemic events without increasing the bleeding risk. [19][20][21] The latter can be assessed by PARIS-MB and PRECISE-DAPT scores in CAD patients under a DAPT after a PCI and by HAS-BLED score developed in AF patients under oral anticoagulation. [21][22][23] These scores underwent receiver operator curve (ROC)-analysis for adverse outcomes in COVID-19 as they include several risk factors, for example, smoking status and obesity, which may be of relevance in the course of COVID-19.
After addressing the available risk scores our further goal was to generate risk assessment models including cardiovascular comorbidities, cardiac biomarkers, and echocardiographic parameters to predict adverse short-and midterm outcomes in patients with SARS-CoV-2 infection.

| Calculation of selected risk scores
Available risk scores for assessment of ACM and/or myocardial ischemic stroke (CHA 2 -DS 2 -VASc), and bleeding complications (HAS-BLED, PARIS-MB, PRECISE-DAPT) were calculated in the study cohort. [16][17][18][19][20][21][22][23] Online calculators for, GRACE  leave-one-out steps were performed using variable selection which resulted in 41 100 different models. Proposed risk assessment scores were obtained from the averaged imputation samples, the AUCs were obtained by averaging results from 100 imputations, standard errors were obtained using Rubin's formula.

| RESULTS
The demographic and clinical characteristics of the patient cohort are listed in Table 1.
The distribution of the study endpoints at 30 days, 6, and 12 months after hospital admission is shown in Table 2. Thromboembolic    Table S2. Table 4 represents the parameters included in generated risk assessment models and multiplication coefficients needed for risk score calculation.

| DISCUSSION
The main findings of the present study are: (1) risk scores originally

| LIMITATIONS
Limitations of this study include the moderately sized patient cohort used for generation of the risk assessment models. Patients included in the study cohort were potentially different in terms of comorbidities before SARS-CoV-2 infection which could have had a severe impact on their prognosis. A limitation of our study is that we cannot discriminate between pre-existing or SARS-CoV-2-induced elevated pulmonary artery pressure, heart failure, elevated NT pro-BNP, and so on. However, even if not directly caused by COVID-19, parameters included in our models show good discriminatory performance for the pre-defined endpoints and thus remain, in our opinion, important factors for prognosis in COVID-19 patients.
Furthermore, this is a retrospective single-center study that lacks an external validation cohort. As the vaccination against COVID-19 started in January 2021 and we recruited patients for the current study in the period from February 2020 until January 2021, we cannot deliver information considering the vaccination status of the patients included in the study. Finally, not all of the parameters included in the study were available for all patients.